Calculate the p-Value from Z-Score in R {https://t.co/7lAhqFAJ1k} #rstats #DataScience
— R-bloggers (@Rbloggers) May 27, 2022
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— R-bloggers (@Rbloggers) May 28, 2022
8 New Books added to Big Book of R {https://t.co/lfDXAGEsl2} #rstats #DataScience
— R-bloggers (@Rbloggers) May 29, 2022
How to add labels at the end of each line in ggplot2? {https://t.co/24cMQAGx0Q} #rstats #DataScience
— R-bloggers (@Rbloggers) May 24, 2022
How to Connect Google Earth Engine with R Shiny {https://t.co/nJWmzUODpp} #rstats #DataScience
— R-bloggers (@Rbloggers) May 26, 2022
How to compare variances in R {https://t.co/H5ksv6OVmw} #rstats #DataScience
— R-bloggers (@Rbloggers) May 29, 2022
Best Books on Data Science with Python {https://t.co/yBVUvbXJ0q} #rstats #DataScience
— R-bloggers (@Rbloggers) May 28, 2022
Stacked-bar alternatives {https://t.co/dmUCCz1ttc} #rstats #DataScience
— R-bloggers (@Rbloggers) May 28, 2022
Bayesian sampling without tears {https://t.co/3Jb1tphh4F} #rstats #DataScience
— R-bloggers (@Rbloggers) May 24, 2022
Survey data chart designs {https://t.co/Vd2XFF2oZ9} #rstats #DataScience
— R-bloggers (@Rbloggers) May 27, 2022
Calling R From Python With rpy2 {https://t.co/DZOt01OPZw} #rstats #DataScience
— R-bloggers (@Rbloggers) May 25, 2022
Survey data I/O with likert {https://t.co/U7DmfhLUmg} #rstats #DataScience
— R-bloggers (@Rbloggers) May 27, 2022
ggblanket: making beautiful ggplot2 visualisation simpler {https://t.co/JzjvIxey0D} #rstats #DataScience
— R-bloggers (@Rbloggers) May 14, 2022
Data Cleaning in R: 2 R Packages to Clean and Validate Datasets {https://t.co/MibZ2lNmMS} #rstats #DataScience
— R-bloggers (@Rbloggers) May 3, 2022
Best Books to Learn R Programming {https://t.co/e6v6GstTSL} #rstats #DataScience
— R-bloggers (@Rbloggers) May 15, 2022
Dashboards in R with Shiny Dashboard {https://t.co/FOwmd72jab} #rstats #DataScience
— R-bloggers (@Rbloggers) May 20, 2022
Mastering Git and GitHub: the hands-on workshop {https://t.co/1ltLOvfBBM} #rstats #DataScience
— R-bloggers (@Rbloggers) May 5, 2022
5 Key Data Visualization Principles Explained – Examples in R {https://t.co/3EnV3282sz} #rstats #DataScience
— R-bloggers (@Rbloggers) May 10, 2022
Creating Dashboards in R {https://t.co/jiKvynHvjy} #rstats #DataScience
— R-bloggers (@Rbloggers) May 20, 2022
Analyze your Twitter timeline with {rtweet} and {lubridate} {https://t.co/iyxJXc04YX} #rstats #DataScience
— R-bloggers (@Rbloggers) May 8, 2022
Shiny in Production (2022) {https://t.co/r8GNtRtKDS} #rstats #DataScience
— R-bloggers (@Rbloggers) May 13, 2022
Hierarchical data visualization with Shiny and D3 {https://t.co/KxkqcTvrHM} #rstats #DataScience
— R-bloggers (@Rbloggers) May 20, 2022
Calculate the p-Value from Z-Score in R {https://t.co/7lAhqFAJ1k} #rstats #DataScience
— R-bloggers (@Rbloggers) May 27, 2022
How to draw heatmap in r: Quick and Easy way {https://t.co/xwXe57Q8FI} #rstats #DataScience
— R-bloggers (@Rbloggers) May 16, 2022
---
title: "RBloggers Top Tweets"
output:
flexdashboard::flex_dashboard:
vertical_layout: scroll
source_code: embed
theme:
version: 4
bootswatch: yeti
css: styles/main.css
---
```{r setup, include=FALSE}
library(flexdashboard)
library(dplyr)
library(httr)
library(lubridate)
library(jsonlite)
library(purrr)
rbloggers <- fromJSON("data/rbloggers.json")
get_tweet_embed <- function(user, status_id) {
url <-
stringr::str_glue(
"https://publish.twitter.com/oembed?url=https://twitter.com/{user}/status/{status_id}&partner=&hide_thread=false"
)
response <- GET(url) %>%
content()
return(shiny::HTML(response$html))
}
```
Column {.tabset .tabset-fade}
-----------------------------------------------------------------------
### Top Tweets - 7 days {.tweet-wall}
```{r}
rblog_7 <- rbloggers %>%
mutate(created_at = as_date(created_at)) %>%
filter(created_at %within% interval(start = today() - 7, end = today())) %>%
slice_max(favorite_count + retweet_count, n = 12)
rblog_7_html <-
map2_chr(rblog_7$screen_name, rblog_7$status_id, get_tweet_embed)
shiny::HTML(stringr::str_glue("{rblog_7_html}"))
```
### Top Tweets - 30 days {.tweet-wall}
```{r}
rblog_30 <- rbloggers %>%
mutate(created_at = as_date(created_at)) %>%
filter(created_at %within% interval(start = today() - 30, end = today())) %>%
slice_max(favorite_count + retweet_count, n = 12)
rblog_30_html <-
map2_chr(rblog_30$screen_name, rblog_30$status_id, get_tweet_embed)
shiny::HTML(stringr::str_glue("{rblog_30_html}"))
```